Portugal

Generation and transformation of images and videos using artificial intelligence have flourished over the past few years. Yet, there are only a few works aiming to produce creative 3D shapes, such as sculptures. Here we show a novel 3D-to-3D topology transformation method using Generative Adversarial Networks (GAN). We use a modified pix2pix GAN, which we call Vox2Vox, to transform the volumetric style of a 3D object while retaining the original object shape. In particular, we show how to transform 3D models into two new volumetric topologies - the 3D Network and the Ghirigoro. We describe how to use our approach to construct customized 3D representations. We believe that the generated 3D shapes are novel and inspirational. Finally, we compare the results between our approach and a baseline algorithm that directly convert the 3D shapes, without using our GAN.

ICCC&#39;20

3D Topology Transformation with Generative Adversarial Networks

topology

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

poster

Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalising what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.


ICCC'20

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.
Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviors that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalizing what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.



"Self-expression is essential to processing our thoughts and feelings and is central to successful mental health therapy. Art therapy provides a wider range of expressive mechanisms than offered through traditional approaches, allowing individuals to process their emotions when traditional therapies prove unsuccessful. Yet, effective expression through art therapy may call on a level of artistic experience that is not available to all.  As such, a lack of expertise or comfort with artistic expression may hinder one's ability to receive needed mental health support. 
 
Creative machines can offer novel therapeutic approaches by offloading the need for creative expertise and opening up creative self-expression to those who lack the corresponding experience. In this paper, we focus on bereavement, and explore a co-creative songwriting system, ALYSIA, as a new form of therapy for those who had recently suffered the loss of a loved one. We evaluate the utility of this creative system in aiding bereaved individuals through several case studies. In addition, we discuss the utility of co-creative systems to the therapeutic context with potential application to  a broad range of therapies."

Co-Creative Songwriting for Bereavement Support

We consider the advent of computational creativity systems that are potentially far superior to human creators, at least in some domains. To do so, we revisit the concepts of P- and H-creativity and propose a social refinement called group-relative creativity (G-creativity). Using this reconceptualization, we explore several critical questions regarding computational creativity’s effect on human creators.

What Happens When a Computer Joins the Group?

"The presentation describes an exploratory study involving humans and computational musical agents. In the study, the system's co-creative behavior
was varied by manipulating a single experimental parameter to engender different co-creative experiences in rhythm duets. In one experimental condition, the system induced a sense of producing divergent material, influencing creative outcomes, and negotiating musical outcomes, three aspects that were consistent with group music creativity. These preliminary findings provide supportive evidence that the system’s parameter can be varied to systematically engender co-creative experiences distinct from creative support."

Engendering co-creative experiences through parametric variation

“Hello” is an interactive cinematic generative artwork that gained traction within the Cultural Adventures research project, an international endeavor between Portugal and Thailand. The encompassing rationale assumed the need of a link connecting both countries, and thus “Hello” was born from the similarities that can be found in commuters that flock to and from both capital cities by train, often more engaged with their mobile phones than with their immediate surroundings – people, train or traversed landscapes –, which constitute more of a shapeless blurred audiovisual background than a sharp attention-grabbing focus. The artwork is also used as a demonstrator of the potential of generative art in producing complex cinematic narrative figurative artworks, yet permeated with interference, chaos and randomness, and how the audience perceives and makes sense of them through the ability to identify and create patterns, to conceptualize, to deal with information overload, and to imagine and build a narrative through abductive reasoning from incomplete yet suggestive information.

Hello, An Interactive Cinematic Generative Artwork

Many machine learning approaches are focused on defining artificial agents able to find solutions to a certain problem given fixed design tools or parameters to optimize. In order to do that, creators must have a certain knowledge of the solution space to define design parameters that ensure enough exploration allowing agents to find its best configuration. However, this approach may limit artificial agents since they are restricted by their initial conditions of a certain design problem. In addition, specific initial conditions also limit them to scale across multiple challenges. In this paper, we explore how the definition of more general design tools can allow artificial agents to better explore the solution space and generalize through multiple design problems. To do that, we compare design artifacts produced by an artificial agent that learns to construct 2D shapes with a fixed number of pieces to another artificial agent that also learns to add or remove pieces from its design proposal. We demonstrate how by allowing more freedom in design, an artificial system is able to produce more novel artifacts with higher performances in multiple scenarios.

Exploring the flexibility of a design tool through different artificial agents

Casual creators are a genre of creativity support tool that use generative systems to empower autotelic and enjoyable amateur creativity. They have been posited as a unique means of democratising creativity through the support of user exploration via system generativity, yet little is known about what casual creators are actually available to wider audiences. We conducted a qualitative analysis of currently available casual creators on the App Store. We found three categories of interaction techniques across 89 commercially available apps that qualified as casual creators, which we describe in their exploration potential, feedback speed, and user autonomy.

Creating a Typology of Casual Creators in the Wild

Being creative in movement-based improvisational environments, such as dance floors, poses a difficult challenge for computers. It is particularly challenging for computers to judge the creativity of inputs and responses in these open-ended domains. LuminAI is an improvisational dance installation where an artificially intelligent agent dances with a user. In this paper, we enable the agent to judge the creativity of dance gestures through the development of creativity metrics, including the novelty, value, and surprise of a gesture. We then use these metrics to implement a lead-and- follow dynamic: when the user is performing less creatively, the agent tries to lead by performing more creatively. A user study is performed to compare the original and lead-and-follow systems, with results showing users found the lead-and-follow system’s gestures lower in quality than the original, more surprising than the original, and found the original system more engaging and influential on their actions.

Creativity Metrics for a Lead-and-Follow Dynamic in an Improvisational Dance Agent

"This paper reports on the automatic selection of short-texts for amplifying the range of a given input's context, e.g. a news headline. Different methods are applied to select a corresponding expression from a list, which should be as semantically related to the input as possible. This study was developed for the Portuguese language, and considered expressions are proverbs, where figurative language is predominant.
The set of explored methods includes some based on word overlap, others on static word embeddings, and also on recent contextual embeddings. To compare the explored methods in this subjective scenario, a survey was answered by humans, who rated the value of the selected expressions in terms of relatedness with regard to the input, and the humoristic value that may arise from this selection. The main conclusion was that simpler approaches, which end up selecting expressions that share words with the headline, are more easily related and considered to be funnier than other more elaborate approaches, which are more focused on the context."

Comparing Different Methods for Assigning Portuguese Proverbs to News Headlines

Since the heydays of music informatics, around the 1950s, the modeling and prediction of musical structures manifested as symbolic representations have been continuously pursued. The operational property of such methods is to provide the conditional distribution over an alphabet -- i.e., the entire collection of unique musical events in a composition or corpus -- given a context -- i.e., a preceding sequence. This distribution unpacks temporal morphologies that support multiple applications for predictive and assisted creative tasks, such as the generation of new musical sequences that retain a structural resemblance to a modeled source. Despite their longstanding tradition, state-of-the-art methodologies for symbolic music modeling are yet to reach the music community. Naive models such as Markov chains, which are known to neglect the fundamental hierarchical nature of musical structure, remain common practice. In this paper, we extensively review existing methodologies for symbolic music representation and modeling, as the first steps towards a study on the resulting balance across familiarity and novelty in generative music applications.

Towards balanced tunes: A review of symbolic music representations and their hierarchical modeling

We describe a method and a proof of concept which allow the generation of rich and engaging dialogues between virtual characters from a formalised plot description. The structure of the dialogue generated borrows from inferential pragmatics, following the Geneva Model of discourse analysis, in order to provide realistic interaction between characters in the narrative. At a higher level, this discourse is organised following heuristics borrowed from narratology theory in order to elicit emotions linked to dramatic tension and thus favour narrative engagement. Besides enriching narrative generation systems embedded within simulation applications, our work also has the potential to be adapted to support engaging interactive dialogues between users and virtual conversational agents in narrative systems.

3D Topology Transformation with Generative Adversarial Networks

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Co-Creative Songwriting for Bereavement Support

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